Compared with single-aperture optical imaging technologies, coded aperture imaging (CAI) could achieve higher light intensity and resolution, thus attracting extensive attention in recent years. However, existing CAI technologies have limitations in low temporal resolution and inferior imaging performance. Deep learning technologies have been widely applied in various signal processing fields due to their powerful capabilities in modeling complex features. Therefore, we develop a convolutional attention mechanism based generative adversarial network (CAM-GAN) model to utilize deep learning technologies to address the above problems, which could adapt to different scenarios and task requirements to improve the effects and stability of CAI. Convolutional attention mechanism module is introduced in this model so that the generator can selectively focus on specific areas of the data to recover details and structures of the original image. On this basis, the network is trained in the form of generative adversarial networks to generate more realistic and higher quality images. Experimental results on public datasets show that compared with other methods, CAM-GAN performs excellently on image quality and achieves the highest peak signal-to-noise ratio, improving by about 0.32 over the suboptimal UNet-GAN algorithm, which fully demonstrates the application potential of deep learning technologies in the CAI field.